backend/README.md

Tribal Knowledge Risk Index & Auto-Correct Planning Engine

A FastAPI service to analyze knowledge concentration (bus factor) and auto-correct sprint plans based on reality gaps.

Setup

1. Install dependencies:

   pip install -r requirements.txt
   

2. Ensure Data is present: Place JSON files in data/.

   • GitHub Dummy Data: prs.json, reviews.json, commits.json, modules.json
   • Jira Dummy Data: jira_sprints.json, jira_issues.json, jira_issue_events.json

Running the Service

Start the server:

python app/main.py

Or:

uvicorn app.main:app --reload

API: http://127.0.0.1:8000

API Endpoints

1. Source System Loading (Run First)

• POST /load_data: Load GitHub data.
• POST /planning/load_jira_dummy: Load Jira data.

2. Computation

• POST /compute: Compute Tribal Knowledge Risks.
• POST /planning/compute_autocorrect: Compute Reality Gaps & Plan Corrections.

3. Features

Tribal Knowledge:

• GET /modules: List modules by risk.
• GET /modules/{id}: Detailed knowledge metrics.

Auto-Correct Planning:

• GET /planning/sprints: List sprints with reality gaps and predictions.
• GET /planning/sprints/{id}: Detailed sprint metrics.
• GET /planning/autocorrect/rules: Learned historical correction rules.

Example Flow

# 1. Load All Data
curl -X POST http://127.0.0.1:8000/load_data
curl -X POST http://127.0.0.1:8000/planning/load_jira_dummy

# 2. Compute Insights
curl -X POST http://127.0.0.1:8000/compute
curl -X POST http://127.0.0.1:8000/planning/compute_autocorrect

# 3. Check "Auto-Correct" Insights
# See the reality gap for the current sprint
curl http://127.0.0.1:8000/planning/sprints